鱼眼车牌脱敏场景数据

随着数据安全和隐私保护法律法规的日益严格，企业在处理车辆相关数据时需要确保车牌等敏感信息的安全。使用鱼眼车牌脱敏数据集，企业可以训练和优化自己的数据脱敏系统，使其满足合规要求，避免因数据泄露而面临的法律风险和巨额罚款。此次数据来源均为我司鱼眼相机采集车采集，不涉及公共数据以及个人数据。一、 图像预处理 将原始图像统一缩放到模型预设的固定尺寸，为640x640像素，以保持网络输入的一致性。并且对图像像素值进行归一化处理，使其落在0到1之间，加速模型收敛并提高训练效果。 二、 特征提取 高效的骨干网络对预处理后的图像进行特征提取，逐步降低特征图分辨率，同时增加通道数，提取多尺度特征。通过特征金字塔网络和路径聚合网络等结构，将不同层次的特征图进行融合，增强模型对不同尺度目标的检测能力。 三、 目标检测 Yolov10采用多尺度检测头，分别对不同分辨率的特征图进行检测。每个检测头包含卷积层和激活函数，用于预测边界框坐标、“车牌”类别概率和置信度得分。 四、后处理 去除冗余框，过滤掉置信度低于50%的预测框。对于同一目标的多余预测框，选择置信度最高的框，抑制其余框，得到最终的检测结果。最后要对边界框调整，对预测的边界框进行微调，使其更准确地拟合目标物体。 五、 输出结果 输出图像中检测到的“车牌”类别、目标框的位置和置信度得分，用于后续的脱敏。

Against the backdrop of increasingly stringent data security and privacy protection laws and regulations, enterprises need to ensure the security of sensitive information such as license plates when processing vehicle-related data. By using the Fisheye License Plate Anonymization Dataset, enterprises can train and optimize their own data anonymization systems to meet compliance requirements and avoid legal risks and heavy fines resulting from data leaks. All data in this dataset is collected by fisheye camera-equipped vehicles of our company, and no public data or personal data is involved.
1. Image Preprocessing
Uniformly resize all raw images to the model's preset fixed size of 640×640 pixels to maintain consistency in network input. Additionally, normalize the pixel values of the images to the range [0, 1] to accelerate model convergence and improve training performance.
2. Feature Extraction
An efficient backbone network performs feature extraction on the preprocessed images, gradually reducing the resolution of feature maps while increasing the number of channels to extract multi-scale features. Structures such as the Feature Pyramid Network (FPN) and Path Aggregation Network (PAN) are used to fuse feature maps from different layers, enhancing the model's ability to detect objects of varying scales.
3. Object Detection
Yolov10 adopts multi-scale detection heads to perform detection on feature maps of different resolutions respectively. Each detection head consists of convolutional layers and activation functions, which are used to predict bounding box coordinates, the probability of the "license plate" category, and confidence scores.
4. Post-Processing
Remove redundant boxes by filtering out prediction boxes with confidence lower than 50%. For duplicate predictions of the same object, select the box with the highest confidence and suppress the remaining ones to obtain the final detection results. Finally, adjust the bounding boxes by fine-tuning the predicted bounding boxes to better fit the target objects accurately.
5. Output Results
Output the detected "license plate" category, the position of the target box, and the confidence score in the image for subsequent anonymization operations.

搜集汇总

背景与挑战

背景概述

该数据集是由宁波博登智能科技有限公司提供的鱼眼车牌脱敏场景数据，包含2324条xlsx格式记录，用于训练和优化数据脱敏系统，确保车牌等敏感信息的安全处理。数据集详细记录了图片ID、尺寸、模型名称等信息，并采用YOLOv10模型进行目标检测和脱敏处理。